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Record W2569082170 · doi:10.1515/hf-2016-0121

Water sorption hysteresis in wood: II mathematical modeling – functions beyond data fitting

2017· article· en· W2569082170 on OpenAlex
Jingbo Shi, Stavros Avramidis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHolzforschung · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHysteresisSorptionInterpretation (philosophy)Biological systemExperimental dataMathematical modelAlgorithmDomain (mathematical analysis)Work (physics)Series (stratigraphy)Nonparametric statisticsApplied mathematicsMathematicsComputer scienceThermodynamicsAdsorptionChemistryEconometricsStatisticsMathematical analysisPhysicsGeologyPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The Preisach model, the mathematical form of the independent domain model, has been used to describe water sorption hysteresis in wood for over 20 years, however, its geometric interpretation has not been fully explored. In this work, it is demonstrated that the geometric interpretation can be used to explain the five experimental hysteresis patterns identified in the first paper of this series. Additionally, a modification to the aforementioned model is suggested that involves a numerical implementation, which avoids the use of unknown parameters. Our approach was evaluated at 25 and 40°C by comparing the predicted 1 st to 4 th order scanning curves with experimental data for Douglas-fir, western red cedar and Aspen. The low prediction errors and well-maintained wiping-out property support the suitability of our approach. Compared to other models found in literature, the presented model has the advantage of high accuracy and easy implementation due to its nonparametric nature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.292
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it